NUIG at TIAD 2021: Cross-lingual Word Embeddings for Translation Inference

Atul Kumar Ojha

doi:http://hdl.handle.net/10379/16973

NUIG at TIAD 2021: Cross-lingual Word Embeddings for Translation Inference

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

Inducing new translation pairs across dictionaries is an important task that facilitates processing and maintaining lexicographical data. This paper describes our submissions to the Translation Inference Across Dictionaries (TIAD) shared task of 2021. Our systems mainly rely on the MUSE and VecMap cross-lingual word embedding mapping to create new translation pairs between English, French and Portuguese data. We also create two regression models based on the graph analysis features. Our systems perform above the baseline systems.

Original language	English (Ireland)
Title of host publication	3rd Conference on Language, Data and Knowledge (LDK 2021)
Place of Publication	Zaragoza, Spain
DOIs	https://doi.org/http://hdl.handle.net/10379/16973
Publication status	Published - 1 Sep 2021

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Ahmadi, Sina; Ojha, Atul Kr.; Banerjee, Shubhanker; and McCrae, John P.

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http://hdl.handle.net/10379/16973

Cite this

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title = "NUIG at TIAD 2021: Cross-lingual Word Embeddings for Translation Inference",

abstract = "Inducing new translation pairs across dictionaries is an important task that facilitates processing and maintaining lexicographical data. This paper describes our submissions to the Translation Inference Across Dictionaries (TIAD) shared task of 2021. Our systems mainly rely on the MUSE and VecMap cross-lingual word embedding mapping to create new translation pairs between English, French and Portuguese data. We also create two regression models based on the graph analysis features. Our systems perform above the baseline systems.",

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AB - Inducing new translation pairs across dictionaries is an important task that facilitates processing and maintaining lexicographical data. This paper describes our submissions to the Translation Inference Across Dictionaries (TIAD) shared task of 2021. Our systems mainly rely on the MUSE and VecMap cross-lingual word embedding mapping to create new translation pairs between English, French and Portuguese data. We also create two regression models based on the graph analysis features. Our systems perform above the baseline systems.

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M3 - Conference Publication

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ER -

NUIG at TIAD 2021: Cross-lingual Word Embeddings for Translation Inference

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this